Storytelling methods on the move
Bibliographic record
Abstract
This article takes up multimedia storytelling and interference as methods on the move in and beyond critical Autism studies and considers their contributions to post and qualitative studies in education. We write as a collective of Autistic and non-Autistic researchers, kin, artists, and educators. We think generatively about the tensions of trying to do anti-normative research through a multimedia storytelling project about Autism justice in education within the confines of academic spaces across differing relationalities to Autism. We situate our method within new materialist ontology, homing in on the concept of “interference”—something we believe has not been done within critical Autism studies before—considering what interference as metaphor and method might offer our analytic approach that diffraction alone might miss. Through analyzing core tensions in the research process and in three films made by Autistic participant-storytellers, we show how Autism flows together and/or collides with storytelling and other post/qualitative methods to make new story forms and modes, and with these, new patterns for understanding Autism and justice in research and education. Our aim is transformative—to open space through post/qualitative research processes for Autistic perspectives and to release multiple stories of Autism into the world. In this we lean into interference and the tangle of research and relationality, power, and possibility for more innovative, just, and critically hopeful knowledge and practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".